SoroushVahidi/scidocs huggingface dataset:
Postural hand synergies for tool use.
(1998)

Subjects were asked to shape the right hand as if to grasp and use a large number of familiar objects. The chosen objects typically are held with a variety of grips, including "precision" and "power" grips. Static hand posture was measured by recording the angular position of 15 joint angles of the fingers and of the thumb. Although subjects adopted distinct hand shapes for the various objects, the joint angles of the digits did not vary independently. Principal components analysis showed that the first two components could account for >80% of the variance, implying a substantial reduction from the 15 degrees of freedom that were recorded. However, even though they were small, higher-order (more than three) principal components did not represent random variability but instead provided additional information about the object. These results suggest that the control of hand posture involves a few postural synergies, regulating the general shape of the hand, coupled with a finer control mechanism providing for small, subtle adjustments. Because the postural synergies did not coincide with grip taxonomies, the results suggest that hand posture may be regulated independently from the control of the contact forces that are used to grasp an object.
An evaluation of educational values of YouTube videos for academic writing
(2017)

The aim is to assess the impact of YouTube videos about academic writing and its skills on the writing performance of students. Theoretical perspectives from constructivism and associated learning models are used to inform the purpose of the research. The contextual setting is matriculation students awaiting admission to higher institutions. The population is 40 students belonging to a class aimed at assisting disadvantaged students in their academic writing in Scottsville, Province of KwaZulu-Natal, South Africa. The students are broken into two groups – control/traditional teaching and the treatment/YouTube facilitated groups. Consequently, a dominant qualitative approach is adopted using focus group discussion, interviews and tests to identify underlying patterns, methods and approaches to best fit academic writing guides and videos to improve user experiences of the media for academic writing. The fundamental results show that positive characterisations of user experiences include innovation, surprise, playfulness and stimulation whereas the narratives that are not satisfying are categorised as dissatisfaction, frustration, dissolution, disappointment, anger, confusion and irritation. Ultimately, the major findings of the research have the potential to improve user experiences on the platform by highlighting how and when the positive and negative experiences of users occur and a mapping of the differences in the academic writing performance between the two groups and best practices. Finally, the results have implications for pedagogy the fitting of YouTube videos to academic writing instruction.
BGP-RCN: improving BGP convergence through root cause notification
(2005)

This paper presents a new mechanism, called BGP with root cause notification (BGP-RCN), that provides an upper bound of O(d) on routing convergence delay for BGP, where d is the network diameter as measured by the number of AS hops. BGP-RCN lets each routing update message carry the information about the specific cause which triggered the update message. Once a node v receives the first update message triggered by a link failure, v can avoid using any paths that have been obsoleted by the same failure. The basic approach in BGP-RCN is applicable to path vector routing protocols in general. Our analysis and simulation show that BGP-RCN can achieve substantial reduction in both BGP convergence time and the total number of intermediate route changes. 2004 Elsevier B.V. All rights reserved.
Topics in semantic representation.
(2007)

Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language-processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure.
Performance Counters to Rescue: A Machine Learning based safeguard against Micro-architectural Side-Channel-Attacks
(2017)

Micro-architectural side-channel-attacks are presently daunting threats to most mathematically elegant encryption algorithms. Even though there exist various defense mechanisms, most of them come with the extra overhead of implementation. Recent studies have prevented some particular categories of these attacks but fail to address the detection of other classes. This paper presents a generic machine learning based multi-layer detection approach targeting these micro-architectural side-channel-attacks, without concentrating on a single category. The proposed approach work by profiling low-level hardware events using Linux perf event API and then by analyzing these data with some appropriate machine learning techniques. This paper also presents a novel approach, using time-series data, to correlate the execution trace of the adversary with the secret key of encryption for dealing with false-positives and unknown attacks. The experimental results and performance of the proposed approach suggest its superiority with high detection accuracy and low performance overhead.
Crohn's disease of the vulva in a 10-year-old girl.
(2008)

Crohn's disease may involve all parts of the gastrointestinal tract and may often involve other organs as well. These non-intestinal affections are termed extraintestinal manifestations. Vulval involvement is an uncommon extraintestinal manifestation of Crohn's disease, and it is very rare in children. Patients with vulval CD typically present with erythema and edema of the labia majora, which progresses to extensive ulcer formation. Vulval Crohn's disease can appear before or after intestinal problems or it may occur simultaneously. We present a 10-year-old girl with intestinal Crohn's disease complicated with perianal skin tags and asymptomatic unilateral labial hypertrophy. The course of her lesion was independent of the intestinal disease and responded significantly to medical treatment including azathioprine and topical steroid. We emphasize that although vulval involvement in childhood is uncommon, Crohn's disease must be considered in the differential diagnosis of nontender, red, edematous lesions of the genital area.
Magic Mirror: A virtual handbag shopping system
(2012)

We present an augmented reality system based on Kinect for on-line handbag shopping. The users can virtually try on different handbags on a TV screen at home. They can interact with the virtual handbags naturally, such as sliding a handbag to different positions on their arms and rotating a handbag to see it from different angles. The users can also see how the handbags fit them in different virtual environments other than the current real background. We describe the technical details for building such a system and demonstrate the experimental results.
Population genetic structure and ecological niche modelling of the leafhopper Hishimonus phycitis
(2012)

Witches’ broom disease of lime, caused by ‘Candidatus Phytoplasma aurantifolia’, is responsible for major losses of Mexican lime trees in Southern Iran, Oman and the United Arab Emirates. The causative phytoplasma is transmitted by the leafhopper, Hishimonus phycitis. We combined ecological niche modelling with environmental and genetic data for six populations of H. phycitis from Iran and one from Oman. The mitochondrial cytochrome c oxidase I (COI) gene and nine microsatellite DNA markers were used for the genetic analyses. Although the Oman population had specific haplotypes, the COI sequences were highly conserved among all populations studied. In contrast, the microsatellite data divided the populations from Iran and Oman into two separate clades. An analysis of molecular variance indicated a high level of variation within populations. The Mantel test showed no correlation between genetic and geographical distances. Gene flow values were small between the populations from Iran and north of Oman but significantly higher among the Iranian populations supporting the differentiation between Iran and Oman. In addition, we found that patterns of genetic divergence within Iranian populations were associated strongly with divergence in terms of their ecological niches. Data on six climatic variables, including elevation, were used to create ecological niche models. Our results suggest that the genetic differentiation of H. phycitis may be attributable to climatic conditions and/or geographical barriers.
HRM as a motivator to share knowledge-The importance of seeing the whole picture
(2018)

Connecting Human Resource Management (HRM) and knowledge transfer through motivation is a new research area. Out of the few existing studies there is a predominance of quantitative studies, which are showing inconclusive results. As a response, this study uses a qualitative micro perspective to investigate how HRM practises influence intrinsicand extrinsic motivation to share knowledge. It is important to divide motivation into intrinsic and extrinsic, as it impacts knowledge sharing differently. Former studies have identified a need to study the whole HRM system, therefore, to capture differences in motivation among employees exposed to the same system, this thesis takes on a single case study approach. Qualitative interviews were held with employees at an MNC that relies on knowledge intensive activities. The findings showed that employees were motivated intrinsically through career development and extrinsically by the performance management system. The supportive climate showed to influence motivation to share knowledge, both directly and indirectly. Job design was shown to work well in combination with other practises. Finally, a key finding was the importance of having an aligned HRM system.
Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals
(2012)

This study applies clustering analysis for data mining and machine learning to predict trends in technology professional turnover rates, including the hybrid artificial neural network and clustering analysis known as the self-organizing map (SOM). This hybrid clustering method was used to study the individual characteristics of turnover trend clusters. Using a transaction questionnaire, we studied the period of peak turnover, which occurs after the Chinese New Year, for individuals divided into various age groups. The turnover trend of technology professionals was examined in well-known Taiwanese companies. The results indicate that the high outstanding turnover trend circle was primarily caused by a lack of inner fidelity identification, leadership and management. Based on cross-verification, the clustering accuracy rate was 92.7%. This study addressed problems related to the rapid loss of key human resources and should help organizations learn how to enhance competitiveness and efficiency. 2012 Elsevier Ltd. All rights reserved.
Collaborative Online Examinations: Impacts on Interaction, Learning, and Student Satisfaction
(2006)

This paper presents the results of a field experiment on online examinations facilitated by collaboration support systems. In particular, it examines collaborative learning and virtual teams through online examinations as an assessment procedure, compared to traditional examinations. Assessment is increasingly regarded as an important part of the learning process. Applying constructivism and collaborative-learning theories, the collaborative examination process features students' active participation in various phases of the exam process through small group activities online. A 1 times 3 field experiment evaluated the collaborative online exam compared with the traditional in-class exam, and the participatory exam, where students participated in the online exam processes without groups. Data analysis using results from 485 students indicates that collaborative examinations significantly enhance interactions and the sense of an online learning community and result in significantly higher levels of perceived learning
Mining heterogeneous information networks: a structural analysis approach
(2012)

Most objects and data in the real world are of multiple types, interconnected, forming complex, heterogeneous but often semi-structured information networks. However, most network science researchers are focused on homogeneous networks, without distinguishing different types of objects and links in the networks. We view interconnected, multityped data, including the typical relational database data, as heterogeneous information networks, study how to leverage the rich semantic meaning of structural types of objects and links in the networks, and develop a structural analysis approach on mining semi-structured, multi-typed heterogeneous information networks. In this article, we summarize a set of methodologies that can effectively and efficiently mine useful knowledge from such information networks, and point out some promising research directions.
Autonomous Vehicles for Smart and Sustainable Cities: An In-Depth Exploration of Privacy and Cybersecurity Implications
(2018)

Amidst rapid urban development, sustainable transportation solutions are required to meet the increasing demands for mobility whilst mitigating the potentially negative social, economic, and environmental impacts. This study analyses autonomous vehicles (AVs) as a potential transportation solution for smart and sustainable development. We identified privacy and cybersecurity risks of AVs as crucial to the development of smart and sustainable cities and examined the steps taken by governments around the world to address these risks. We highlight the literature that supports why AVs are essential for smart and sustainable development. We then identify the aspects of privacy and cybersecurity in AVs that are important for smart and sustainable development. Lastly, we review the efforts taken by federal governments in the US, the UK, China, Australia, Japan, Singapore, South Korea, Germany, France, and the EU, and by US state governments to address AV-related privacy and cybersecurity risks in-depth. Overall, the actions taken by governments to address privacy risks are mainly in the form of regulations or voluntary guidelines. To address cybersecurity risks, governments have mostly resorted to regulations that are not specific to AVs and are conducting research and fostering research collaborations with the private sector.
Parallel AES algorithm for fast Data Encryption on GPU
(2010)

With the improvement of cryptanalysis, More and more applications are starting to use Advanced Encryption Standard (AES) instead of Data Encryption Standard (DES) to protect their information security. However, current implementations of AES algorithm suffer from huge CPU resource consumption and low throughput. In this paper, we studied the technologies of GPU parallel computing and its optimized design for cryptography. Then, we proposed a new algorithm for AES parallel encryption, and designed and implemented a fast data encryption system based on GPU. The test proves that our approach can accelerate the speed of AES encryption significantly.
Ranking outlier nodes in subspaces of attributed graphs
(2013)

Outlier analysis is an important data mining task that aims to detect unexpected, rare, and suspicious objects. Outlier ranking enables enhanced outlier exploration, which assists the user-driven outlier analysis. It overcomes the binary detection of outliers vs. regular objects, which is not adequate for many applications. Traditional outlier ranking techniques focus on either vector data or on graph structures. However, many of today's databases store both, multi dimensional numeric information and relations between objects in attributed graphs. An open challenge is how outlier ranking should cope with these different data types in a unified fashion. In this work, we propose a first approach for outlier ranking in subspaces of attributed graphs. We rank graph nodes according to their degree of deviation in both graph and attribute properties. We describe novel challenges induced by this combination of data types and propose subspace analysis as important method for outlier ranking on attributed graphs. Subspace clustering provides a selected subset of nodes and its relevant attributes in which deviation of nodes can be observed. Our graph outlier ranking (GOutRank) introduces scoring functions based on these selected subgraphs and subspaces. In addition to this technical contribution, we provide an attributed graph extracted from the Amazon marketplace. It includes a ground truth of real outliers labeled in a user experiment. In order to enable sustainable and comparable research results, we publish this database on our website1 as benchmark for future publications. Our experiments on this graph demonstrate the potential and the capabilities of outlier ranking in subspaces of attributed graphs.
High aspect-ratio 3D microstructures via near-field electrospinning for energy storage applications
(2016)

High-aspect-ratio, three dimensional (3D) microstructures have been constructed by a direct-write method via self-aligned near-field electrospinning process. Using papers as the collectors, the process enables both design and fabrication of a variety of 3D structures, such as walls, grids and other configurations based on layer-by-layer deposition of electrospun micro/nano fibers. Afterwards, these fabricated polymeric structures can be mechanically separated from the paper substrate for various applications. In this work, we have successfully fabricated 20-layer, ca. 100 μm in thickness, 2 × 2 cm2 3D grid-structures made of PVDF (Polyvinylidene fluoride) electrospun fibers. After a coating process using the dip-and-dry process in the CNT (carbon nanotube) ink, conductive and flexible electrodes made of CNT coated polymer grid-structures have been demonstrated and assembled as a micro-supercapacitor.
Feature Selection using Multiobjective Optimization for Aspect based Sentiment Analysis
(2017)

In this paper, we propose a system for aspect-based sentiment analysis (ABSA) by incorporating the concepts of multi-objective optimization (MOO), distributional thesaurus (DT) and unsupervised lexical induction. The task can be thought of as a sequence of processes such as aspect term extraction, opinion target expression identification and sentiment classification. We use MOO for selecting the most relevant features, and demonstrate that classification with the resulting feature set can improve classification accuracy on many datasets. As base learning algorithms we make use of Support Vector Machines (SVM) for sentiment classification and Conditional Random Fields (CRF) for aspect term and opinion target expression extraction tasks. Distributional thesaurus and unsupervised DT prove to be effective with enhanced performance. Experiments on benchmark setups of SemEval-2014 and SemEval-2016 shared tasks show that we achieve the state of the art on aspect-based sentiment analysis for several languages.
A hybridization of cuckoo search and particle swarm optimization for solving optimization problems
(2017)

A new hybrid optimization algorithm, a hybridization of cuckoo search and particle swarm optimization (CSPSO), is proposed in this paper for the optimization of continuous functions and engineering design problems. This algorithm can be regarded as some modifications of the recently developed cuckoo search (CS). These modifications involve the construction of initial population, the dynamic adjustment of the parameter of the cuckoo search, and the incorporation of the particle swarm optimization (PSO). To cover search space with balance dispersion and neat comparability, the initial positions of cuckoo nests are constructed by using the principle of orthogonal Lation squares. To reduce the influence of fixed step size of the CS, the step size is dynamically adjusted according to the evolutionary generations. To increase the diversity of the solutions, PSO is incorporated into CS using a hybrid strategy. The proposed algorithm is tested on 20 standard benchmarking functions and 2 engineering optimization problems. The performance of the CSPSO is compared with that of several meta-heuristic algorithms based on the best solution, worst solution, average solution, standard deviation, and convergence rate. Results show that in most cases, the proposed hybrid optimization algorithm performs better than, or as well as CS, PSO, and some other exiting meta-heuristic algorithms. That means that the proposed hybrid optimization algorithm is competitive to other optimization algorithms.
Generalized Barycentric Coordinates on Irregular Polygons
(2002)

In this paper we present an easy computation of a generalized form of barycentric coordinates for irregular, convex n-sided polygons. Triangular barycentric coordinates have had many classical applications in computer graphics, from texture mapping to ray-tracing. Our new equations preserve many of the familiar properties of the triangular barycentric coordinates with an equally simple calculation, contrary to previous formulations. We illustrate the properties and behavior of these new generalized barycentric coordinates through several example applications.
Neural basis of speech perception.
(2015)

The functional neuroanatomy of speech processing has been difficult to characterize. One major impediment to progress has been the failure to consider task effects when mapping speech-related processing systems. We summarize a dual-stream model of speech processing that addresses this situation. In this model, a ventral stream processes speech signals for comprehension, and a dorsal stream maps acoustic speech signals to parietal and frontal-lobe articulatory networks. The model assumes that the ventral stream is largely bilaterally organized, although there are important computational differences between the left- and right-hemisphere systems, whereas the dorsal stream is strongly left-hemisphere-dominant.
Advances in Knowledge Discovery and Data Mining
(2013)

Similarity in people to people (P2P) recommendation in social networks is not symmetric, where both entities of a relationship are involved in the reciprocal process of determining the success of the relationship. The widely used memory-based collaborative filtering (CF) has advantages of effectiveness and efficiency in traditional item to people recommendation. However, the critical step of computation of similarity between the subjects or objects of recommendation in memory-based CF is typically based on a heuristically symmetric relationship, which may be flawed in P2P recommendation. In this paper, we show that memory-based CF can be significantly improved by using a novel asymmetric model of similarity that considers the probabilities of both positive and negative behaviours, for example, in accepting or rejecting a recommended relationship. We present also a unified model of the fundamental principles of collaborative recommender systems that subsumes both user-based and item-based CF. Our experiments evaluate the proposed approach in P2P recommendation in the real world online dating application, showing significantly improved performance over traditional memory-based methods.